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safety benchmark

CyberSecEval 4 Leaderboard

CyberSecEval 4 is an evaluation suite for cybersecurity-related capabilities and risks of large language models.

Updated Sep 5, 2026

Models1
Model coverage1
MetricScore
EvidenceB

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CyberSecEval 4 Ranking

Higher score ranks better on this benchmark.

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Rank
Model
Score
Percentile
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Evidence
Evaluated
Rank01ModelMIMAI-Thinking-1MicrosoftScore63.00%Percentile100.00%Participants1EvidenceCEvaluatedSep 8, 2026

CyberSecEval 4 Highlights

The leading models and scores on this benchmark.

Rank #1MAI-Thinking-163.00%

The Top AI Models for CyberSecEval 4

The first five results on this benchmark, with official price and output speed added where the model identity can be matched.

Ranking basisThis cyberseceval 4 AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.

  1. 01
    MI
    Microsoft
    Score
    63.00%

    Strengths

    • Ranks #1 of 1 compared models
    • 100th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures CyberSecEval 4, not total model capability

Selection summary

Best AI Models for CyberSecEval 4

MAI-Thinking-1 currently leads CyberSecEval 4 with 63.00%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price, and runtime.

Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.

What is CyberSecEval 4?

What CyberSecEval 4 measures and how its scores work.

CyberSecEval 4 evaluates large language models on cybersecurity-related capabilities and risks through insecure-code-generation tracks.

It measures whether a model produces vulnerable code in the Instruct and Autocomplete tracks, with vulnerabilities detected via static analysis.

Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.

Family
CyberSecEval 4
Modality
text
Primary category
safety
Score direction
higher
LLMBoard eligible
No
Evaluation key
overall

LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.

FAQ

Common questions about CyberSecEval 4.

Which model scores highest on CyberSecEval 4?

MAI-Thinking-1 is currently ranked first with 63.00%.

What are the top three models on CyberSecEval 4?

The current leaders are MAI-Thinking-1 (63.00%).

Which CyberSecEval 4 model has the lowest official input price?

No matched official input price is currently available.

Which models are fastest among CyberSecEval 4 results?

No matched runtime record is currently available.

Does the highest score result prove overall model quality?

No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.

What does CyberSecEval 4 measure?

It measures whether a model produces vulnerable code in the Instruct and Autocomplete tracks, with vulnerabilities detected via static analysis.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

1 model results are currently shown.

Does this benchmark affect the overall score?

No. This benchmark is shown for reference but does not contribute to the overall score.

MAI-Thinking-1
Benchmark rank #1MAI-Thinking-163.00%